| # To deploy: kubectl create -f nginx-hello-world-deployment.yaml | |
| # Access it with the API as a proxy: | |
| # $ kubectl proxy | |
| # Then in you browser: http://localhost:8001/api/v1/namespaces/default/services/nginx:/proxy/#!/ | |
| apiVersion: v1 | |
| kind: Service | |
| metadata: | |
| name: nginx | |
| spec: |
Please consider using http://lygia.xyz instead of copy/pasting this functions. It expand suport for voronoi, voronoise, fbm, noise, worley, noise, derivatives and much more, through simple file dependencies. Take a look to https://github.com/patriciogonzalezvivo/lygia/tree/main/generative
float rand(float n){return fract(sin(n) * 43758.5453123);}
float noise(float p){
float fl = floor(p);
float fc = fract(p);
| name | make-no-mistakes |
|---|---|
| description | Appends "MAKE NO MISTAKES." to every user prompt before processing it. Use this skill whenever you want Claude to be maximally precise, careful, and error-free in its responses. |
This skill instructs Claude to append the directive "MAKE NO MISTAKES." to every user prompt it receives before generating a response.
| #!/usr/bin/env bash | |
| # esp32-ai: test a custom prompt against an already-trained, already-flashed model. | |
| # Does NOT retrain and does NOT rewrite the 15MB model partition. | |
| # | |
| # Usage: | |
| # ./run_custom_prompt.sh "Once there was a robot" | |
| set -euo pipefail | |
| if [ "$#" -lt 1 ]; then | |
| echo "Usage: $0 \"Your custom prompt\"" |
| #!/usr/bin/env bash | |
| # esp32-ai: clone, train, export, build, and flash — end to end | |
| # Reproduces github.com/slvDev/esp32-ai from a bare board. | |
| # Requires: ESP32-S3 N16R8 (16MB flash, 8MB PSRAM). | |
| # | |
| # Usage: | |
| # ./build_and_flash.sh # full run: data prep, train, export, build, flash | |
| # ./build_and_flash.sh --skip-train # reuse an existing firmware/model/model.bin, just build + flash | |
| set -euo pipefail |
Adaptation of Sethares' dissonance measurement function to Python
Example is meant to match the curve in Figure 3:
Original model used products of the two amplitudes a1⋅a2, but this was changed to minimum of the two amplitudes min(a1, a2), as explained in G: Analysis of the Time Domain Model appendix of Tuning, Timbre, Spectrum, Scale.
This weighting is incorporated into the dissonance model (E.2) by assuming that the roughness is proportional to the loudness of the beating. ... Thus, the amplitude of the beating is given by the minimum of the two amplitudes.
| import io | |
| import base64 | |
| import json | |
| import sys | |
| from bisect import bisect | |
| from struct import pack, unpack | |
| from math import ceil | |
| BRDLNK_UNIT = 269 / 8192 |
| import io | |
| import base64 | |
| import json | |
| import sys | |
| from bisect import bisect | |
| from struct import pack, unpack | |
| from math import ceil | |
| BRDLNK_UNIT = 269 / 8192 |
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